Artificial Intelligence-Based Volatility Forecasting and Global Financial Market Integration

This study examined how Artificial Intelligence (AI)-based volatility forecasting can assist in describing financial integration among the most important global equity markets. As cross-border capital flows develop and information moves more rapidly, stock markets have become more interrelated. This makes volatility forecasting important for perceiving risk and how it spreads. This study analyzed the daily index data of both developed and emerging markets to create AI-based volatility estimates. The main focus was on techniques that can capture nonlinear behavior and changes in the market over time. The study also measured whether the volatility movements forecasted by AI matched genuine blueprints of financial integration across markets. The results showed that AI-based models were very reactive during times of market uncertainty. They gave consistent signs about risk spillover and how international markets tighten up. The findings revealed that market associations become much stronger during economic stress events. This indicates that volatility is not restricted to specific countries but is increasingly a general issue. The study emphasized the importance of using AI in risk forecasting, monitoring different markets, and managing global portfolios. The results encouraged market regulators and financial institutions to accept data-driven approaches for real-time reviews of market stability and timely detection of risks. This research contributed to the developing field that combines machine learning with financial integration by providing insights into how analytical volatility factors reflect changing global market associations.